OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms
Venue: arXiv cs.DC 2023
Year: 2023

DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, jointly considering node-level heterogeneity of edge devices, and application-specific accuracy and performance requirements. Our proposed approach combinatorially optimizes heterogeneity-aware workload partitioning and dynamic accuracy configuration of DNN models to ensure performance and accuracy guarantees. We tested our approach on an edge cluster of Odroid XU4, Raspberry Pi4, and Jetson Nano boards and achieved an average gain of 41.52% in performance and 5.2% in output accuracy as compared to state-of-the-art workload distribution strategies.
A Multilayered Security Infrastructure for Connected Vehicles -- First Lessons from the Field
Venue: arXiv cs.CR 2023
Year: 2023

Connected vehicles are vulnerable to manipulation and a broad attack surface can be used to intrude in-vehicle networks from anywhere on earth. In this work, we present an integrated security infrastructure comprising network protection, monitoring, incident management, and counteractions, which we built into a prototype based on a production car. Our vehicle implements a Software-Defined Networking Ethernet backbone to restrict communication routes, network anomaly detection to make misbehavior evident, virtual controller functions to enable agile countermeasures, and an automotive cloud defense center to analyse and manage incidents on vehicle fleets. We present first measurements and lessons learned from operating the prototype: many network attacks can be prevented through software-defined access control in the backbone; anomaly detection can reliably detect misbehavior but needs to improve on false positive rate; controller virtualization needs tailored frameworks to meet in-car requirements; and cloud defence enables fleet management and advanced countermeasures. Our findings indicate attack mitigation times in the vehicle from 257 ms to 328 ms and from 2,168 ms to 2,713 ms traversing the cloud.
Scene Graph Conditioning in Latent Diffusion
Venue: arXiv cs.CV 2023
Year: 2023

Diffusion models excel in image generation but lack detailed semantic control using text prompts. Additional techniques have been developed to address this limitation. However, conditioning diffusion models solely on text-based descriptions is challenging due to ambiguity and lack of structure. In contrast, scene graphs offer a more precise representation of image content, making them superior for fine-grained control and accurate synthesis in image generation models. The amount of image and scene-graph data is sparse, which makes fine-tuning large diffusion models challenging. We propose multiple approaches to tackle this problem using ControlNet and Gated Self-Attention. We were able to show that using out proposed methods it is possible to generate images from scene graphs with much higher quality, outperforming previous methods. Our source code is publicly available on https://github.com/FrankFundel/SGCond
Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes
Venue: arXiv cs.CV 2023
Year: 2023

Although significant progress has been made, achieving place recognition in environments with perspective changes, seasonal variations, and scene transformations remains challenging. Relying solely on perception information from a single sensor is insufficient to address these issues. Recognizing the complementarity between cameras and LiDAR, multi-modal fusion methods have attracted attention. To address the information waste in existing multi-modal fusion works, this paper introduces a novel three-channel place descriptor, which consists of a cascade of image, point cloud, and fusion branches. Specifically, the fusion-based branch employs a dual-stage pipeline, leveraging the correlation between the two modalities with latent contacts, thereby facilitating information interaction and fusion. Extensive experiments on the KITTI, NCLT, USVInland, and the campus dataset demonstrate that the proposed place descriptor stands as the state-of-the-art approach, confirming its robustness and generality in challenging scenarios.
Enhanced Edge-Perceptual Guided Image Filtering
Venue: arXiv cs.CV 2023
Year: 2023

Due to the powerful edge-preserving ability and low computational complexity, Guided image filter (GIF) and its improved versions has been widely applied in computer vision and image processing. However, all of them are suffered halo artifacts to some degree, as the regularization parameter increase. In the case of inconsistent structure of guidance image and input image, edge-preserving ability degradation will also happen. In this paper, a novel guided image filter is proposed by integrating an explicit first-order edge-protect constraint and an explicit residual constraint which will improve the edge-preserving ability in both cases. To illustrate the efficiency of the proposed filter, the performances are shown in some typical applications, which are single image detail enhancement, multi-scale exposure fusion, hyper spectral images classification. Both theoretical analysis and experimental results prove that the powerful edge-preserving ability of the proposed filter.
On the Relevance of Temporal Features for Medical Ultrasound Video Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Many medical ultrasound video recognition tasks involve identifying key anatomical features regardless of when they appear in the video suggesting that modeling such tasks may not benefit from temporal features. Correspondingly, model architectures that exclude temporal features may have better sample efficiency. We propose a novel multi-head attention architecture that incorporates these hypotheses as inductive priors to achieve better sample efficiency on common ultrasound tasks. We compare the performance of our architecture to an efficient 3D CNN video recognition model in two settings: one where we expect not to require temporal features and one where we do. In the former setting, our model outperforms the 3D CNN - especially when we artificially limit the training data. In the latter, the outcome reverses. These results suggest that expressive time-independent models may be more effective than state-of-the-art video recognition models for some common ultrasound tasks in the low-data regime.
On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data
Venue: arXiv cs.CV 2023
Year: 2023

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://github.com/GDAOSU/Transferability-Remote-Sensing
Metric Ensembles For Hallucination Detection
Venue: arXiv cs.CL 2023
Year: 2023

Abstractive text summarization has garnered increased interest as of late, in part due to the proliferation of large language models (LLMs). One of the most pressing problems related to generation of abstractive summaries is the need to reduce "hallucinations," information that was not included in the document being summarized, and which may be wholly incorrect. Due to this need, a wide array of metrics estimating consistency with the text being summarized have been proposed. We examine in particular a suite of unsupervised metrics for summary consistency, and measure their correlations with each other and with human evaluation scores in the wiki_bio_gpt3_hallucination dataset. We then compare these evaluations to models made from a simple linear ensemble of these metrics. We find that LLM-based methods outperform other unsupervised metrics for hallucination detection. We also find that ensemble methods can improve these scores even further, provided that the metrics in the ensemble have sufficiently similar and uncorrelated error rates. Finally, we present an ensemble method for LLM-based evaluations that we show improves over this previous SOTA.
Microscaling Data Formats for Deep Learning
Venue: arXiv cs.LG 2023
Year: 2023

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in real-time situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence's length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what's possible in online, skeleton-based action recognition. The code for InfoGCN++ is publicly available at https://github.com/stnoah1/infogcn2 for further exploration and validation.
RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets
Venue: arXiv cs.CV 2023
Year: 2023

We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without any inference costs. Specifically, given a pre-trained model, RefConv applies a trainable Refocusing Transformation to the basis kernels inherited from the pre-trained model to establish connections among the parameters. For example, a depth-wise RefConv can relate the parameters of a specific channel of convolution kernel to the parameters of the other kernel, i.e., make them refocus on the other parts of the model they have never attended to, rather than focus on the input features only. From another perspective, RefConv augments the priors of existing model structures by utilizing the representations encoded in the pre-trained parameters as the priors and refocusing on them to learn novel representations, thus further enhancing the representational capacity of the pre-trained model. Experimental results validated that RefConv can improve multiple CNN-based models by a clear margin on image classification (up to 1.47% higher top-1 accuracy on ImageNet), object detection and semantic segmentation without introducing any extra inference costs or altering the original model structure. Further studies demonstrated that RefConv can reduce the redundancy of channels and smooth the loss landscape, which explains its effectiveness.
Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment
Venue: arXiv cs.CL 2023
Year: 2023

Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience. As the large language models (LLMs) have exhibited remarkable in-context learning capabilities, a question arises as to whether the task of OIE can be effectively tackled with this paradigm? In this paper, we explore solving the OIE problem by constructing an appropriate reasoning environment for LLMs. Specifically, we first propose a method to effectively estimate the discrepancy of syntactic distribution between a LLM and test samples, which can serve as correlation evidence for preparing positive demonstrations. Upon the evidence, we introduce a simple yet effective mechanism to establish the reasoning environment for LLMs on specific tasks. Without bells and whistles, experimental results on the standard CaRB benchmark demonstrate that our $6$-shot approach outperforms state-of-the-art supervised method, achieving an $55.3$ $F_1$ score. Further experiments on TACRED and ACE05 show that our method can naturally generalize to other information extraction tasks, resulting in improvements of $5.7$ and $6.8$ $F_1$ scores, respectively.
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
Venue: arXiv cs.CV 2023
Year: 2023

Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise editing. Prior attempts to address this challenge by introducing video-2D representations encounter significant difficulties with large-scale motion- and view-change videos, especially in human-centric scenarios. To overcome this, we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation, where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field. To provide consistent and controllable editing, we propose the image-based video-NeRF editing pipeline with a set of innovative designs, including multi-view multi-pose Score Distillation Sampling (SDS) from both the 2D personalized diffusion prior and 3D diffusion prior, reconstruction losses, text-guided local parts super-resolution, and style transfer. Extensive experiments demonstrate that our method, dubbed as DynVideo-E, significantly outperforms SOTA approaches on two challenging datasets by a large margin of 50% ~ 95% for human preference. Code will be released at https://showlab.github.io/DynVideo-E/.
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
Venue: arXiv cs.RO 2023
Year: 2023

If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios might not be present in the robot's own training data. We propose SuSIE, a method that leverages an image-editing diffusion model to act as a high-level planner by proposing intermediate subgoals that a low-level controller can accomplish. Specifically, we finetune InstructPix2Pix on video data, consisting of both human videos and robot rollouts, such that it outputs hypothetical future "subgoal" observations given the robot's current observation and a language command. We also use the robot data to train a low-level goal-conditioned policy to act as the aforementioned low-level controller. We find that the high-level subgoal predictions can utilize Internet-scale pretraining and visual understanding to guide the low-level goal-conditioned policy, achieving significantly better generalization and precision than conventional language-conditioned policies. We achieve state-of-the-art results on the CALVIN benchmark, and also demonstrate robust generalization on real-world manipulation tasks, beating strong baselines that have access to privileged information or that utilize orders of magnitude more compute and training data. The project website can be found at http://rail-berkeley.github.io/susie .
Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior Attribute
Venue: arXiv cs.CR 2023
Year: 2023

The proliferation of network attacks poses a significant threat. Researchers propose datasets for network attacks to support research in related fields. Then, many attack detection methods based on these datasets are proposed. These detection methods, whether two-classification or multi-classification, belong to single-label learning, i.e., only one label is given to each sample. However, we discover that there is a noteworthy phenomenon of behavior attribute overlap between attacks, The presentation of this phenomenon in a dataset is that there are multiple samples with the same features but different labels. In this paper, we verify the phenomenon in well-known datasets(UNSW-NB15, CCCS-CIC-AndMal-2020) and re-label these data. In addition, detecting network attacks in a multi-label manner can obtain more information, providing support for tracing the attack source and building IDS. Therefore, we propose a multi-label detection model based on deep learning, MLD-Model, in which Wasserstein-Generative-Adversarial- Network-with-Gradient-Penalty (WGAN-GP) with improved loss performs data enhancement to alleviate the class imbalance problem, and Auto-Encoder (AE) performs classifier parameter pre-training. Experimental results demonstrate that MLD-Model can achieve excellent classification performance. It can achieve F1=80.06% in UNSW-NB15 and F1=83.63% in CCCS-CIC-AndMal-2020. Especially, MLD-Model is 5.99%-7.97% higher in F1 compared with the related single-label methods.
Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset
Venue: arXiv cs.CR 2023
Year: 2023

Cybersecurity has become a significant issue in the digital era as a result of the growth in everyday computer use. Cybercriminals now engage in more than virus distribution and computer hacking. Cyberwarfare has developed as a result because it has become a threat to a nation's survival. Malware analysis serves as the first line of defence against an attack and is a significant component of cybercrime. Every day, malware attacks target a large number of computer users, businesses, and governmental agencies, causing billions of dollars in losses. Malware may evade multiple AV software with a very minor, cunning tweak made by its designers, despite the fact that security experts have a variety of tools at their disposal to identify it. To address this challenge, a new dataset called the Obfuscated Malware Dataset (OMD) has been developed. This dataset comprises 40 distinct malware families having 21924 samples, and it incorporates obfuscation techniques that mimic the strategies employed by malware creators to make their malware variations different from the original samples. The purpose of this dataset is to provide a more realistic and representative environment for evaluating the effectiveness of malware analysis techniques. Different conventional machine learning algorithms including but not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme Gradient Boosting (XGBOOST) etc are applied and contrasted. The results demonstrated that XGBoost outperformed the other algorithms, achieving an accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%.
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Venue: arXiv cs.LG 2023
Year: 2023

The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfolios with the increased complexity of the AAS task. In practice, probably the most common way to choose the algorithms for the portfolio is a greedy selection of the algorithms that perform well in some reference tasks of interest. We set out in this work to investigate alternative, data-driven portfolio selection techniques. Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms. We evaluate two distinct meta-representation techniques (SHAP and performance2vec) for selecting complementary portfolios from a total of 324 different variants of CMA-ES for the task of optimizing the BBOB single-objective problems in dimensionalities 5 and 30 with different cut-off budgets. We test two types of portfolios: one related to overall algorithm behavior and the `personalized' one (related to algorithm behavior per each problem separately). We observe that the approach built on the performance2vec-based representations favors small portfolios with negligible error in the AAS task relative to the virtual best solver from the selected portfolio, whereas the portfolios built from the SHAP-based representations gain from higher flexibility at the cost of decreased performance of the AAS. Across most considered scenarios, personalized portfolios yield comparable or slightly better performance than the classical greedy approach. They outperform the full portfolio in all scenarios.